{"id":"W1926765961","doi":"10.1021/ja309294u","title":"Specific <sup>12</sup>C<sup>β</sup>D<sub>2</sub><sup>12</sup>C<sup>γ</sup>D<sub>2</sub>S<sup>13</sup>C<sup>ε</sup>HD<sub>2</sub> Isotopomer Labeling of Methionine To Characterize Protein Dynamics by <sup>1</sup>H and <sup>13</sup>C NMR Relaxation Dispersion","year":2012,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research","keywords":"Chemistry; Isotopomers; Protein dynamics; NMR spectra database; Relaxation (psychology); Deuterium; Population; Crystallography; Spectral line; Molecular dynamics; Computational chemistry; Molecule; Atomic physics; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaepi_narrow","sts","research_integrity"],"category_scores_codex":[0.005271147,0.00600213,0.007738915,0.001229309,0.003261513,0.001260247,0.006818878,0.003930005,0.0007603682],"category_scores_gemma":[0.00256659,0.005599745,0.005896638,0.005833075,0.004643485,0.004274641,0.003923222,0.01123546,0.000425099],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005797788,"about_ca_system_score_gemma":0.001270249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004767659,"about_ca_topic_score_gemma":0.00001379202,"domain_scores_codex":[0.9692865,0.001640222,0.009365027,0.005662967,0.006785288,0.007259979],"domain_scores_gemma":[0.9751936,0.002858649,0.008163746,0.006365061,0.002797567,0.004621347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002192899,0.004020833,0.003749837,0.001411405,0.002448819,0.00009419418,0.01249101,0.1170534,0.7994722,0.0003593738,0.03175,0.02495599],"study_design_scores_gemma":[0.00869997,0.001410465,0.0003038936,0.003169585,0.002572549,0.001300525,0.02067463,0.32218,0.5996781,0.001412464,0.03140451,0.007193352],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9709129,0.002406452,0.009467419,0.006803606,0.0001517338,0.005055003,0.003168085,0.001373506,0.0006613141],"genre_scores_gemma":[0.9573296,0.008441205,0.01901442,0.003511996,0.005158112,0.001506216,0.00231634,0.001811324,0.0009108119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2051266,"threshold_uncertainty_score":0.9997765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01089565366773717,"score_gpt":0.241597470284774,"score_spread":0.2307018166170369,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}